Modern wireless communications networks include many different network topologies comprising heterogeneous mixtures of macrocell, microcell, picocell, and femtocell resources. At the highest level of wireless coverage, a macrocell provides cellular service for a relatively large physical area, often in areas where network traffic densities are low. In more dense traffic areas, a macrocell may act as an overarching service provider, primarily responsible for providing continuity for service area gaps between smaller network cells. In areas of increased traffic density, microcells are often utilized to add network capacity and to improve signal quality for smaller physical areas where increased bandwidth is required. Numerous picocells and femtocells generally add to network capacity for even smaller physical areas in highly populated metropolitan and residential regions of a larger data communications network.
As would be understood by those skilled in the Art, in all wireless service provider networks, macrocells typically provide the largest wireless coverage area for licensed frequency spectra, followed by microcells, then picocells, and lastly femtocells. By way of example, in a typical wireless data communications network, a macrocell base station may provide a wireless coverage area ranging between one to five kilometers, radially from the center of the cell; a microcell base station may provide a coverage area ranging between one-half to one kilometer radially; a picocell base station may provide a coverage area ranging between 100 to 500 meters radially; and a femtocell base station may provide a coverage area of less than 100 meters radially. Each of these network cell or base station types is generally configured to connect with a particular service provider network using various common wireline communications technologies, including, but not limited to: fiber optic, twisted pair, powerline, and/or coaxial cable (joining cells to a backhaul network).
In modern data communications networks comprising many different types of network cells, it is becoming more and more complex to properly provision, manage, and optimize distributed network resources in a cost effective manner. Traditional radio access network parameter management is carried out through a centralized controller where radio resource parameters are stored in individual network resource Management Information Bases (MIBs). These master MIBs are usually maintained in a central Network Operations Center (NOC) at some remote network location, such as a backhaul portion of a distributed communications network. As would be understood by those skilled in the Art, in most digital communications networks, the backhaul portion of the network may comprise the intermediate, generally wireline, links between a backbone of the network, and the sub-networks or network cells located at the periphery of the network. For example, cellular user equipment communicating with a cell base station may constitute a local sub-network or cell. Whereas the network connection between the base station and the rest of the world initiates with a link to the backhaul portion of a access provider's network (e.g., via a point of presence).
Initially, these master MIBs may be populated with default parameter settings, including, but not limited to, any of the following network parameters/settings listed in Table 1. Those skilled in the Art would be aware of the purpose and provisioning assignments necessary for managing any of these optional MIB parameters:
TABLE 1Network Parameters:Network ID and addressBackhaul routing informationBase station network permissionsNetwork management parametersBase Station RF Parameters:Operating channelRadio configurationTransmit power settingsTransmit and receive timing/framing parametersAccess parametersProtocol Parameters:Message retry limitsRetry timersGeneral Base Station Parameters:Base station locationTime base informationHardware initialization parametersRedundancy settingsBase Station Airlink Parameters:Base station IDNeighbor listBroadcast channel messagesHandover parameters
The listing of network parameters in Table 1 could be more exhaustive or more consolidated, depending on the network technology employed and the resources being provisioned within various network cells, however, these parameters give a high level example of the type of network base station parameters that may need to be designated (e.g., as a component of a MIB) and/or frequently optimized within a network base station to allow it to operate properly and efficiently within a given network area. In practice, many of the parameters in master MIBs are designated by the equipment manufacturers whom may preset upwards of 95% of the individual parameters for a given network resource, leaving the remaining percentage of undesignated parameters to depend on specific hardware configurations and device capabilities, as well as network service provider specified preferences. Some parameters that can be designated by service provider preference may include parameters related to a licensed frequency band and field-specific optimization parameters, including: transmit power, neighbor lists, handover thresholds, any of the parameters listed above in Table 1, as well as any other network parameters that may be manually provisioned as result of network engineering and manual network optimization activities.
As one example of a manual optimization process, in many existing cellular networks, service providers utilize mobile network resource testing vehicles to periodically gather information to help them manually compensate for the effects of real-world radio frequency isolation contributors and neighboring interference sources. These compensation techniques may allow for service providers to effectively reprovision and/or optimize network resources by determining new, real-time parameter adjustments that can be manually applied to and employed by various network radio access nodes (RANs), such as macrocell, microcell, picocell, and femtocell base stations. Unfortunately, these mobile testing solutions require manual operation as well as manual radio operating parameter adjustment at network resource sites. These largely iterative solutions are also expensive to routinely employ, and they are too infrequently utilized to keep up with dynamically changing network resources and environments. Accordingly, manual testing and compensation techniques (e.g., those employing parameter updating in master MIBs) are inadequate solutions for effectively determining and neutralizing many of the negative effects associated with dynamically changing network environments, which are becoming more and more complex with the rapid deployment of an increasing numbers of smaller network cells in evolving wireless communications networks (e.g., with the evolution of 4G communications networks).
These evolving network topologies may result in robust mixtures of network cell coverages within regions of overlapping wireless service. In particular, many modern, low power base stations (e.g., picocell and femtocell devices base stations) are readily transportable within an existing communications network by end users. This mobility can create a situation where many smaller cell base stations may be moved to unpredictable locations within a network where their operation could potentially produce substantial interference to surrounding network infrastructure, unless their maximum radio power levels were constrained to reduce unwanted instances of network interference. These ad-hoc cell deployments are difficult to model, because end users often do not register their devices' new locations with their local service providers. As a result, modern mobile network resource optimization solutions are not utilized frequently enough to timely learn of their presence and then compensate for their interfering affect within a particular network cell (e.g., by adjusting network resource parameters at various network cell base stations).
In modern data communications networks, typically after a network resource (e.g., such as a RAN) is discovered in a network by a controller device or service provider entity, it can only be managed if its local MIB is available and is accessible by the resource's operations management software. If the network resource's MIB is not available, a vendor must be contacted to obtain the required compatible MIB. This manual process may take hours to days for completion. Until the proper MIB is obtained, and optionally custom-provisioned, the discovered RAN is not manageable. Another problem that can arise is if a master MIB for a discovered device is not of the correct version associated with a particular RAN type. In this case, a network controller may not be able to coherently manage the features of the RAN, particularly as a given network evolves (e.g., with the addition of many new smaller network cells).
Accordingly, it would be helpful to be able have improved systems and methods that can facilitate the management of individual RAN resource parameters in a self-optimizing wireless network (e.g., in self-organizing cellular networks). It would be helpful if these systems and methods could support default power up parameter assignment, uncomplicated control and override of specific parameters, defined ranges and permissible operating conditions for subsequent self-optimization operations, localized update of defined parameters via self-optimization algorithms, and network rollback to known operating states as necessary to assure system stability (e.g., in response to a network failure condition). It would also be helpful to be able to utilize existing network resources (e.g., distributed RANs) to account for actual network resource operating conditions, in order to facilitate accurate provisioning and determination of network radio operating parameters between and amongst various network base stations. It would further be advantageous if these improved solutions enhanced radio access network performance by employing optimized RAN management utilities in a dynamic network environment. These improved, self-optimizing network utilities would effectively automate processes that were previously largely manual tasks, thereby reducing the level of required human intervention for successful network operations. This would result in operational and/or deployment savings and it would provide for many other performance, quality, and operational benefits. The importance of these benefits would be readily understood by those familiar with the multitude of benefits commonly associated with self-organized network (SoN) solutions.